Spatial Modeling of Cardiovascular Disease in Kenya

The study conducts an assessment of the spatial distribution of cardiovascular diseases (CVD) in Kenya by integrating spatial modeling techniques and spatial autocorrelation measures. CVDs, which refer to disorders of the heart and blood vessels, have surpassed communicable diseases as the leading cause of morbidity and mortality worldwide, posing a critical public health concern, especially in low- and middle-income countries (LMICs) where resources remain limited. A growing body of global evidence has revealed marked geographical disparities in CVD incidence, prompting investigations into small-area spatial distribution patterns. This study employed both global and local spatial autocorrelation measures to analyze CVD prevalence across Kenyan counties. The Global Moran’s I statistic was used to assess the overall degree of spatial clustering, while the Local Moran’s I identified significant clusters of high and low prevalence, alongside spatial outliers. Additionally, the Getis-Ord Gi* statistic was applied to detect statistically significant hotspots and coldspots, revealing important spatial patterns in disease prevalence. Spatial regression models were compared using the Lagrange Multiplier (LM) test, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) for model selection. The Spatial Lag Model (SLM) demonstrated superior performance over the Spatial Error Model (SEM) and the Spatial Durbin Model (SDM), achieving a Rao’s score (RSlag) of 16.449 and an adjusted score (adjRSlag) of 12.181, both statistically significant at the 5% level. The SLM also recorded the lowest AIC and BIC values at -380.09 and -361.80, respectively, confirming its suitability in capturing spatial dependence in the data. The findings revealed significant spatial clustering of CVD prevalence, with distinct high-risk and low-risk regions across the country. High body mass index (HBMI), tobacco use, and poor dietary habits emerged as major risk factors driving CVD prevalence, while urbanization and economic development were associated with lower disease burdens. The study highlights the importance of incorporating spatial analysis in public health planning to inform targeted interventions, optimize resource allocation, and enhance community health education campaigns aimed at promoting heart-healthy lifestyles.